Stochastic Description of Biochemical Networks

Abstract

Conventional deterministic chemical kinetics often breaks down in the small volume of a living cell where cellular species (e.g., genes, mRNAs, etc.) exist in discrete, low copy numbers and react through reaction channels whose timing and order is random. In such an environment, a stochastic chemical kinetics framework that models species abundances as discrete random variables is more suitable. The resulting models consist of continue-time discrete-state Markov chains. Here we describe how such models can be formulated and numerically simulated, and we present some of the key analysis techniques for studying such reactions.